Method and system for real-time, false positive resistant, load independent and self-learning anomaly detection of measured transaction execution parameters like response times

ABSTRACT

A combined transaction execution monitoring, transaction classification and transaction execution performance anomaly detection system is disclosed. The system receives and analyzes transaction tracing data which may be provided by monitoring agents deployed to transaction executing entities like processes. In a first classification stage, parameters are extracted from received transaction tracing data, and the transaction tracing data is tagged with the extracted classification data. A subsequent measure extraction stage analyzes the classified transaction tracing data and creates corresponding measurements which are tagged with the transaction classifier. A following statistical analysis process maintains statistical data describing the long term statistical behavior of classified measures as a baseline, and also calculates corresponding statistical data describing the current statistical behavior of the classified measures. The statistical analysis process detects and notifies significant deviations between the statistical distribution of baseline and current measure data. A subsequent anomaly alerting and visualization stage processes those notifications.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/857,788, filed on Jul. 24, 2013. The entire disclosure of the aboveapplication is incorporated herein by reference.

FIELD

The present disclosure relates to the analysis of performance monitoringand tracing data of individual distributed transactions to identifydistinct classes of transactions, extracting classification and measuredata from transaction performance monitoring and tracing data and todetect statistical relevant anomalies of transaction executionperformance measures.

BACKGROUND

Real-time monitoring of the performance of productive softwareapplications, especially of high transaction volume and revenuegenerating applications like e-commerce applications has become crucialfor the successful operation of such applications, because evenshort-term performance or functionality issues potentially haveconsiderable impact on the customer base and the revenue of suchapplications.

However, the monitoring and alerting demands of application operationteams, responsible for unobstructed, outage-minimizing operation of suchapplication which require information about the overall situation of theapplication, deviates from the demands of software architects andprogrammers responsible for fast identification and elimination ofprogram code causing the performance or functionality problems, whichrequire high-detail transaction execution performance information, downto the granularity of individual method executions.

Existing monitoring systems aiming to trace and monitor individualtransactions at the granularity level required by software architectsand programmers reached a level of unobtrusiveness in terms of operationand of efficiency in terms of monitoring caused overhead that allows toemploy such monitoring systems in the day-to-day operation of large,high-volume productive software applications. A detailed description ofsuch a monitoring system can be found in U.S. Pat. No. 8,234,631 “Methodand system for tracing individual transactions at the granularity levelof method calls throughout distributed heterogeneous applicationswithout source code modifications” by Bernd Greifeneder et al. which isincorporated herein by reference in its entirety.

Albeit such systems provide the data required by software engineers toidentify and fix punctual performance problems, the granularity of theprovided data is way to fine to allow operation teams a fast and precisejudgment of the overall situation of a monitored application.

Especially in high-load scenarios where applications receive hundreds oreven thousands of requests per minute resulting in the execution ofhundreds and thousands of complex transactions per minute, a situationwhich is typical for modern e-commerce applications, conventional,threshold based alerting systems are inadequate due to the large numberof generated false-positive alerts. Reason for this is the large numberof transactions, which increases the possibility of performanceoutliers, which only reflect a negligible fraction of the performedtransactions. Such outliers are also negligible from applicationoperational and financial point of view, but would still triggerundesired alerts. Even baseline oriented alerting systems, usinghistoric performance measurements to establish expected values forcurrent and future measurements run into the same problem because theyuse the baseline threshold to create alerts based on singlemeasurements.

Application operation teams mostly rely on infrastructure monitoringsystems, which monitor the utilization of infrastructure resources, likeCPU, disc or memory usage of the host computers running the monitoredapplications to determine the health state of an application and todecide appropriate countermeasures to detected or anticipatedperformance problems. As an example, the memory consumption of a processrunning an application may be monitored. In case the memory consumptionexceeds a specific limit, the application process is restarted. Althoughthis approach fulfills the needs of application operation, and may incase of an existing clustering/failover system cause no lost or failedtransactions, it does not provide analysis data that helps to identifyand fix the root cause (e.g. memory leak) of the problem.

The tendency to outsource and concentrate operation of such applicationsto external data-centers or even to cloud computing system adds anotherdimension of complexity to the problem of identifying the root cause ofperformance or functionality problems of productive applications,because it may blur the relationship between an application and thecomputing environment used to execute the application. In suchenvironments, computing infrastructure like host computer systems, orprocesses running virtual machines, may be dynamically assigned toapplications depending on the current load situation.

As a consequence, a monitoring and alerting system is required thatfulfills the needs of both software development and maintenance teamsand of application operation teams. It should on the one hand providetransaction tracing and monitoring data at finest granularity level,allowing the detection of the code causing the performance problem andon the other hand produce outlier and false-positive resistant, reliablealerts as required by application operation teams.

The desired solution should also be able to cope with outsourcedapplications or multi-application data-centers, where the monitoringsystem should be capable to identify and monitor a multiple ofapplications or application components, like e.g. a product searchcomponent or a product purchase component. Additionally, the desiredsolution should reduce the required configuration effort to identify andmonitor applications or application components to a minimum.

This section provides background information related to the presentdisclosure which is not necessarily prior art.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

A monitoring and alerting system capable for large volume, productiveapplications is disclosed, that analyzes individual transactionsprocessed by the monitored application. The analysis result allowsdetecting in real-time if the performance behavior of the application asa whole, not of an individual transaction has changed.

Exemplary embodiments of the current disclosure provide mechanisms toextract classification parameters, like parts of an URL of a webrequest, identifying transactions directed to a specific application ora component of a specific application, like a “product search” or a“purchase” component, and to tag incoming transactions with theextracted classification parameters.

Some variants of those embodiments may further extract measuresdescribing the performance behavior of monitored transactions, which arealso tagged with the transaction classification extracted from thetransaction trace data.

Yet other variants of those embodiments may calculate statisticalparameters from historic classified measurements, describing theperformance behavior of the application or application componentidentified by the classification value during a reference period, whichmay be used as baseline.

Again other variant embodiments may calculate statistical parametersfrom current classified measurements, describing the current performancebehavior of the application or application component identified by theclassification value, and may use statistical parameters describingcurrent and baseline performance behavior to identify statisticalrelevant deviations between baseline and current performance behavior.

Still other variants of those embodiments may use quantile estimationsfor the statistical description of baseline and current behavior, andperform statistical tests using those quantile estimations to detectdeviations between baseline and current behavior that take the qualityof the quantile estimation into account.

Yet other variants of those embodiments may use multiple quantileestimators using different reset mechanisms for quantile estimation datato detect fast performance trend changes.

Other embodiments of the current disclosure may forward detectedstatistical relevant deviations between baseline and current performancemeasurements to an alerting processor, which uses alerting rules e.g.considering current and past detected deviations to create alerts.

Yet other embodiments of the current disclosure may analyze incomingclassified transaction tracing data to detect and mark failedtransaction executions. Return codes indicating unexpected transactionresults or detected uncaught exceptions during transaction executionsmay be used to mark traced transactions as failed.

Variants of those embodiments may use the transaction failure indicatorto calculate failure rates of a set of historic, baseline transactionsand a set of recently executed transaction representing the currentfailure rate condition of an application or application component.

Yet other variants of those embodiments may use statistical methods todescribe the occurrence of relatively rare events like transactionfailures to create a statistical description of the baseline failurerate, and to create a statistical description of the current failurerate and to use both statistical descriptions to determine if astatistically significant deviation between the baseline and the currenterror rate occurred.

Some other variants of those embodiments may assume Poisson or Binominaldistribution of observed failure rates and use appropriate statisticalprocesses and methods to generate parameters describing baseline andcurrent failure expectancy and detect statistically significantdeviations.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 shows an overview of a monitoring and anomaly detection systemconsisting in at least on agent providing transaction executionmonitoring and tracing data, and a monitoring node, dedicated to analyzeand classify received transaction monitoring and tracing data, to detectanomalies in transaction execution performance measurements and to alerton such detected anomalies.

FIGS. 2A-C show transaction trace data records which may be used torepresent transaction tracing and monitoring data in form of datarecords representing whole end-to-end transactions, data recordsrepresenting individual thread executions of a monitored transaction anddata records to represent individual method executions within a threadexecution.

FIG. 3 shows a classification rule record which may be used to store aclassification extraction rule, which may be used to extract transactionclassification data out of transaction tracing and monitoring data.

FIG. 4 depicts the process of extracting classification data fromtransaction tracing and monitoring data using a set of classificationextraction rules.

FIG. 5 shows a measure extraction rule record which may be used to storea measure extraction rule that is used to extract measure data fromtransaction tracing and monitoring data.

FIG. 6 shows a measure record which may be used to store measure dataextracted from transaction tracing data together with correspondingtransaction classification data.

FIG. 7 shows the process of extracting measure data from classifiedtransaction tracing and monitoring data and storing it in correspondingmeasure records together with corresponding transaction classificationdata.

FIGS. 8A-B show measure distribution and distribution parameter recordswhich may be used to store statistical data representing the statisticaldistribution of measures of a specific type and classification.

FIG. 9 shows a statistical statement record which may be used to storeresults of a statistical test comparing different statisticaldistributions of measures with a specific measure type andclassification.

FIGS. 10A-B conceptually show the process of cyclically updating thestatistical baseline data used by statistical test and the process toupdate statistical data representing the current distribution of theobserved measure, including the statistical test to detect deviationsbetween baseline and current data.

FIG. 11 shows an alert transition rule record which may be used to storerules that evaluate statistical statement records and to change thestate of an alert corresponding to those statistical statement records.

FIG. 12 shows an alert record which may be used to store an alert thatrepresents a detected transaction execution performance anomaly.

FIG. 13 conceptually shows the processing of statistical statements bythe alert processing stage to update corresponding alerts according tomatching alert transition rules.

FIGS. 14A-C show the statistical density function of measuredtransaction response times.

FIG. 15 shows a process that updates statistical baseline data in formof quantile estimations representing the statistical distribution of thebaseline of a specific measure.

FIG. 16 depicts a process that updates statistical data in form ofquantile estimations that represents the current statisticaldistribution of a measure.

FIG. 17 shows a process that performs a quantile based statistical testto detect statistically relevant deviations between correspondingcurrent and baseline measurements.

FIGS. 18A-B describe a situation, where a specific measure of a specifictransaction class gradually and slowly approaches its correspondingbaseline and afterwards fast and significant exceeds the baseline,leading to a slow reacting current baseline estimation causing delayeddetection of the deviation. Additionally it shows a solution to thisproblem using a second baseline estimator which is reset morefrequently.

FIGS. 19A-B show the process of maintaining a second, more frequentlyreset baseline estimator and the process of additionally using thesecond baseline estimator to detect anomalies in situations as describedin FIG. 18.

FIG. 20 shows a failure detection rule record, which may be used todefine and store rules to determine if a received transaction tracedescribes a successful or failed transaction execution.

FIG. 21 conceptually shows the processing of multiple failure detectionrule records on transaction trace data to determine if the describedtransaction was successful or failed.

FIG. 22 shows the process of generating measures describing the amountof performed transactions (successful and failed) and the amount offailed transactions for a specific time interval.

FIG. 23 shows the process of determining a baseline value failure rateusing a specific historic reference time interval

FIG. 24 shows the process of calculating a failure rate specific forcurrent transaction executions and comparing it to a failure raterepresentative for a historic reference time interval.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

The described embodiments and variants are directed to performancemonitoring and alerting systems capable to handle highly complex, highvolatile and high load scenarios. The possibility of false-positivealerts and the required configuration effort are minimized. Thedescribed systems use statistical methods to compare a set of historictransaction measurements representing a performance baseline, with a setof currently or recently executed transactions representing the currentperformance situation of a monitored application. The result of thiscomparison is used to decide if an alert is triggered or not.

Referring now to FIG. 1 which shows an overview of a monitoredapplication process with a deployed agent that generates and sendstransaction tracing data, and a monitoring node 111 which receivestransaction tracing data, incrementally builds end-to-end tracing dataand performs various evaluations and analyses on the created end-to-endtracing data, including a statistical, baseline oriented performanceanomaly analysis. In the example embodiment, the monitoring node 111 isfurther defined as a transaction monitor application which resides on amonitor computer.

A host computer 101 executes an application process 102 which may bepart of a more complex distributed application consisting in multiple,communicating application processes, executed on different host computersystems to fulfill incoming requests. The application process isinvolved in the execution of distributed transactions. An agent 108 isdeployed to the application process 102, which instruments specificmethods 104 that are called during transaction executions with entry 105and exit 106 sensors. Those sensors are triggered on the execution ofsuch instrumented methods 104 by a thread 103 and create notificationsin form of transaction events 107 signaling start and end of theexecution. Additionally, such sensors may capture and send methodexecution context data, like type and value of method parameters andreturn values. As an example, a method dedicated to receive and handleHTTP requests may be instrumented with an entry sensor that captures thereceived URL. An agentId 109 may be assigned to the agent 108 whichuniquely identifies the agent 108.

The agent receives transaction events 107 from the instrumented sensorsand forwards them to a monitoring node 111 via a connecting computernetwork 110. Multiple agents, deployed to multiple application processesrunning on different hosts may send their transaction events to themonitoring node 111. The monitoring node forwards received transactionevents to an event correlator 112 which uses correlation data containedin the received transaction events to create end-to-end transactiontracing data. Agents and sensors may be injected into the monitoredsystem by augmenting application specific code with monitoring specificcode that performs acquisition of measurement data and execution contextcorrelation data and communication with a monitoring node 111. Theinjection may either be performed permanent by manipulating source codeof the monitored application and recompiling it, or it may be injectedon the fly, during runtime of the monitored application. Runtimeinjection may be performed using byte-code instrumentation techniquesfor byte-code executing parts of the monitored application like Java™,.NET or PHP processes as described in U.S. Pat. No. 8,234,631. It mayalso be performed by manipulating and injecting JavaScript™ code intoHTML pages produced by the monitored applications and displayed by webbrowsers used to interact with the monitored application according tothe teachings of U.S. patent application Ser. No. 13/722,026 “Method AndSystem For Tracing End-To-End Transaction, Including Browser SideProcessing And End User Performance Experience” and U.S. Ser. No.14/056,016 “Method And System For Browser Based, Non-Intrusive MeasuringOf End-User Perceived Performance Of Individual Third Party ResourceRequests” both by Bernd Greifeneder et al. which are incorporated hereinby reference in their entirety.

Sensors may also be implementing by hooking or modifying calls to theruntime environment of the monitored process indicating the execution ofmonitored methods in case of e.g. PHP or web server processes. Thosehooks or modifications may be used to recognize the execution ofspecific methods, to capture execution context data like methodparameters or return values and to send the captured data to amonitoring node 111 in form of transaction events 107. Sensors may alsoprovide portions of end-to-end tracing data in cooperation withcall-stack sampling technologies as described in U.S. patent applicationSer. No. 13/455,764 “Method and System for Transaction ControlledSampling of Distributed Heterogeneous Transactions without Source CodeModifications” by Bernd Greifeneder et al. which is incorporated hereinby reference in its entirety.

The event correlator 112 forwards finished end-to-end transactiontracing data to the transaction classifier 113, which extractsclassification data form received transactions using classificationrules 301 stored in a classification rule repository 115. The purpose ofthe classification process is to detect and group transactionsperforming similar or the same tasks, to get homogenous groups oftransactions which under normal conditions show similar performancebehavior. Statistical tests for such homogenous groups can be performedin a stricter fashion, because a “normal” behavior of such homogenousgroups can be predicted exacter, and deviations from a “normal” behaviorcan be detected earlier.

The processing of an exemplary, URL based classification rule for aspecific end-to-end transaction trace would e.g. first fetch the URLreceived with the request that initiated the transaction and thenanalyze the data of the URL to extract classification data. An URLconsists, among other parts, in the name of the server to which therequest was sent, a path identifying an addressed resource or componenton the server, and a query string influencing the calculation of theresponse created by the addressed component. The URL basedclassification rule may e.g. specify to extract server name and path ofthe URL and use it as classification. Using this exemplaryclassification rule, following URLs“http://www.myshop.com/products/list?page=1” and“http://www.myshop.com/products/search?name=prod1” would createclassifications “http://www.myshop.com/products/list” and“http://www.myshop.com/products/search”. All transactions addressing the“list” component would fall into the first category and all transactionsaddressing the “search” component would fall into the second category.

The grouping of monitored transactions into homogenous groups accordingto application component or functionality also provides more finegrained monitoring and alerting results. In case the performancebehavior of a specific transaction class significantly changes, alertscan be triggered that identify the component assigned with thetransaction class which eases the isolation of potential root causes ofthe performance problem.

The transaction tracing data is tagged with the extracted classificationvalue and further processed by a measure extractor 116 which evaluatesthe transaction tracing data according to measure extraction rules 501in a measure extraction rule repository 118 to create measure records601 describing specific performance parameters of the transaction. Anexample measure would be the total response time of the transaction. Themeasure records 601 are also tagged with the transaction classificationand stored in a measure repository 117.

A transaction class measure anomaly detector 119 fetches the classifiedmeasures from the measure repository and cyclically creates and updateshistoric baseline data describing the statistical distribution ofmeasures with the same classification and measure type. As an example,one baseline distribution description may be created for all historictotal response time measures of classification “application1/component1”and another baseline description may be created for all historic totalresponse time measures of classification “application1/component2”. Thisbaseline description data may e.g. consider all measurements from thelast day, and may be recalculated daily. Those baseline descriptions arestored in a baseline repository 122 in form of measure distributionrecords 801.

The transaction class measure anomaly detector 119 also creates similarstatistical distribution descriptions for a set of current measures,which are stored in a current repository 120. The set of currentmeasures may be defined as all measures of a specific classification andtype that are younger than a specific time threshold, e.g. five minutes,or the last n measures of a specific classification and type, or allmeasures of a specific classification and type since a statistic testcomparing baseline and current measures of the specific classificationand type provided a result with a significance above a certain level.

Statistical tests are cyclically performed that compare correspondingbaseline and current distribution descriptions to identify statisticallysignificant baseline deviations. The results of those tests are storedin a statistical statement repository 121 in form of statisticalstatement records 901.

An anomaly alerting and visualization module 123 fetches statisticalstatements records 901 and evaluates alerting rules based on new andhistoric statistical statements to determine if an alert should betriggered.

Transaction processing and analysis is performed by the monitoring nodein real-time and in parallel and pipe-lined fashion. While a first setof transaction traces is processed by the measure extractor 116 togenerate measures describing the transactions, a second set oftransaction traces may be processed by the transaction classifier 113,while a third set of transaction traces describing currently ongoingtransactions is still incrementally created by the event correlator 112.Anomaly detection and alerting works concurrently and independent to thetransaction trace creation and analysis processing pipeline. Thestatistical analysis and alerting is performed during runtime of themonitored application and while the monitored application is running andprocessing transactions. The reported anomaly states represent the stateof the monitored system at the point of time of the last statisticaltest. The optimal test execution frequency is influenced by the load ofthe monitored application (high load causes increases the number ofstatistical samples and allows higher frequencies) and the computationaland mathematical complexity of the test (higher complexity requires moreCPU cycles and potentially restricts to lower frequencies). A typicaltest interval which represents a good tradeoff between both requirementsis 1 minutes. It allows to execute also more complex tests and itguarantees that the reported anomaly status is not older than oneminute. This guarantees that the visualized or notified performance orfailure rate anomaly state is not older than one minute.

The transaction repository 114 contains transaction traces of allmonitored transactions performed by the monitored application. Withincreasing operation time of the monitoring system, and with rising loadof the monitored application, the amount of transaction traces rises,until it becomes unpractical or even impossible to keep all transactiontraces in main memory. In such situations, transactions with an olderexecution time, e.g. older than one hour, may be persistently stored,e.g. in a database or in a file, and removed from the main memory.Access to transaction traces may be provided in a transparent way, i.e.for a client requesting transactions, it is not noticeable if thetransaction trace is fetched from main memory or from a persistentstorage.

The same situation may occur for measure records stored in the measurerepository 117. Also in this case, older measure records may be removedfrom main memory and stored in a persistent storage. For clientsrequesting measures (e.g. to calculate baseline data), it is notnoticeable if the measure records are fetched from main memory or from apersistent storage.

Data records dedicated to the storage of individual end-to-endtransaction traces are described in FIG. 2. Root thread execution datarecords 201, as shown in FIG. 2 a and thread execution data records 210as shown in FIG. 2 b, are used to represent individual thread executionsforming an end-to-end transaction. Root thread execution data recordsrepresent a thread execution that handles a request that enters amonitored application. Such a root thread execution may spawn otherthread executions which are represented by thread execution data records210 that are linked with the root thread execution data record as childthread executions. Those child thread executions may in turn spawn otherthread executions which are linked as child thread executions of thosethreads. The linking between parent and child thread executions ismodelled using method list entries 220 describing methods executed by athread. Each method list entry 220 is part of the method execution list205/213 of a root thread execution data record 201 or a thread executiondata record 210. In case a monitored method spawns a thread, an entry inthe child thread execution list 224 of the corresponding method listentry 220 is created which refers to the corresponding thread executiondata record 210 describing the spawned thread. A root thread executiondata record 201 may be used to represent a whole end-to-end transactiontrace.

A root thread execution data record 201 and a thread execution datarecord 210 may contain but is not limited to a threadId 202/211 thatuniquely identifies a specific thread execution performed by a specificprocess on a specific host computer system, thread measurements 204/212providing measurements describing the thread execution as a whole, likename or execution priority of the thread, and method execution list205/213 containing a list of monitored method executions performed bythe thread, in a way that allows to reconstruct nesting level andsequence of the method executions.

A root thread execution data record 201 may in addition contain aclassification field holding the classification of the representedend-to-end transaction. Such a root thread execution data record 201 mayalso contain a failure indicator 206 which is set by a process thatanalyzes the root thread execution data record and all its connectedthread execution records and method list entries to determine if thetransaction execution terminated with a failure or not.

A method list entry 220 as depicted in FIG. 2 c may be used to representan individual monitored method execution performed as part of a threadexecution.

A method list entry 220 may contain but is not limited to a methodId 221identifying the type of the executed method by the name of the method,the name of the class declaring the method and the signature of themethod, execution context data 222 containing captured method parametervalues and method return values, and execution measure data 223containing performance measures describing the method execution, likeexecution duration or CPU usage of the method execution, and a childthread execution list 224, containing references to thread executiondata records 210 describing thread executions that the method executiondescribed by the method list entry has spawned and started.

End-to-end tracing data in form of a root thread execution data recordand a set of linked thread execution data records, all containing methodlist entries is incrementally created by the event correlator 112 out ofreceived transaction events 107 according to the teachings of U.S. Pat.No. 8,234,631. A detailed description of the correlation process can befound there.

A classification rule record 301 which may be used to extractclassification data out of end-to-end transaction traces is shown inFIG. 3. Such a classification rule 301 may contain but is not limited toa transaction filter rule 302 which may be used to filter transactionsfor which the classification rule record can be applied, aclassification parameter extraction rule 303, describing a way toextract transaction parameters like specific method parameter values ofspecific method calls, and a classification parameter conversion rule304 describing a way to convert transaction parameters extractedaccording to the classification parameter extraction rule into aclassification value.

An exemplary classification rule record to extract a transactionclassification value out of the URL of an incoming HTTP request may bedefined as follows. The transaction rule filter may filter transactionswith a root thread execution data record having a root level monitoredmethod (i.e. the first monitored method executed by the thread)dedicated to the handling of an incoming HTTP request. Theclassification parameter extraction rule would select the parametervalue of the top level method of the initial thread execution of thetransaction that provided the URL of the HTTP request and provide it asclassification parameter. The classification parameter conversion rulemay extract and concatenate server name and path of the URL and returnit as classification.

FIG. 4 shows the process to determine the classification of an incomingend-to-end transaction as performed by the transaction classifier 113.

The process starts with step 401, when the transaction classifier 113receives a new transaction trace. Subsequent step 402 fetches the firstclassification rule record 301 from the classification rule repository115 and step 403 evaluates the transaction filter rule 302 to check ifthe classification rule record matches the received transaction. In casethe evaluation rejects the transaction, the process continues with step405 which fetches the next classification rule record. In case a nextclassification rule is available, the process continues with step 403.Otherwise, step 409 applies a default classification value to thetransaction and the process ends with step 411. The defaultclassification value may be an empty string or a string “<default>” todistinguish such transaction traces from successfully classifiedtransactions. If step 403 determines that the transaction trace matchesthe filter rule 302, the process continues with step 406, whichevaluates the classification parameter extraction rule 303 and theclassification parameter conversion rule 304 to create a validtransaction classification. In case no valid classification value couldbe determined, the process continues with step 405 to fetch the nextclassification rule record. Otherwise, the process continues with step410, which applies the extracted classification to the classificationfield 203 of the root thread execution data record 201. The process thenends with step 411.

A measure extraction rule record 501, which may be used to determinewhich measures should be extracted from transaction traces is shown inFIG. 5. Such measure extraction rules may extract measures from specificmethod executions reported by a transaction trace. Alternatively,measure extraction rules may extract statistical data of all method orthread executions forming a transaction trace, like the number of threadexecutions of a transactions, the number of all method calls, calls of aspecific method identified by method name, name of the class declaringthe method and method parameters, number of exceptions thrown or caughtduring transaction execution.

A measure extraction rule record 501 may contain but is not limited to atransaction filter 502 which is used to determine if a measureextraction rule should be evaluated for a specific transaction trace, atransaction context filter 503, defining transaction context parametersthat have to be met for the measure extraction, like nesting level of amethod from which measures should be extracted, method parameters orparent/child relationships of the thread executing the method, a measurevalue extraction rule, defining which performance measure should beextracted from captured data describing the method execution or whichmethod executions or exception count should be calculated, and a measuretype 505, describing the kind of extracted measure, like “total responsetime”, “exception count” etc.

An exemplary measure extraction rule 501 dedicated to extract the totalresponse time of transactions initiated by incoming HTTP requests woulde.g. provide a transaction filter for transaction traces with a rootthread execution data record 201 with a top level method list entrydescribing a method to handle incoming HTTP requests. Examples for suchmethods used by the servlet framework provided by Oracle Java™ would bemethods like “doGet” declared by class “java.servlet.http.HttpServlet”and derived classes.

The transaction context filter 503 would specify that the top levelthread execution of the transaction represented by its root threadexecution data record 201 should be examined, and of this data record,the top level method list entry 220 should be selected, which initiallyreceived the HTTP request and which returned the corresponding HTTPresponse.

The measure value extraction rule 504 would define to extract theexecution time of the method execution identified by the transactioncontext filter 503 and provide it as a measure. The value of the measuretype would e.g. be “HTTP response time” to indicate that the time tocreate a corresponding HTTP response for a received HTTP request ismeasured.

Measure records 601, which may be used to store measures extracted fromtransaction traces according to measure extraction rule records 501, areshown in FIG. 6. A measure record 601 may contain but is not limited toa measure type 602 identifying the kind of the measure, a classification603 as derived by the transaction classifier from the transaction tracedescribed by the measure record, a timeslot 604 representing the pointin time or time range for which the measure value 605 is valid, and ameasure value 605 containing the extracted numerical measure value.

The process of extracting measure records 601 from classifiedtransaction traces as performed by the measure extractor 116 isdescribed in FIG. 7.

The process starts with step 701, when the measure extractor 116receives a classified transaction trace and continues with step 702 thatfetches the first measure extraction rule record 501 from the measureextraction rule repository 118. Subsequent step 703 evaluates thetransaction filter 502 on the transaction trace to determine if thefetched measure extraction rule record 501 can be applied to thereceived transaction trace. In case the evaluation results in no match,the process continues with step 711 which fetches the next measureextraction rule record 501. In case no next rule is available, theprocess ends with step 713. Otherwise processing continues with step703.

In case transaction filter evaluation performed by step 703 indicates amatch, the process continues with step 705 and evaluates the transactioncontext filter 503 to find a matching transaction context. In case nomatching transaction context was found, processing continues with step711. Otherwise, the process subsequently executes step 707 to extract ameasure value according to the measure value extraction rule 504 of themeasure extraction rule record 501. In case no appropriate measure valuecould be extracted, the process continues with step 711. Otherwise, step709 is executed which creates a new measure record 601. The measure type602 is set to the measure type 505 of the currently processed measureextraction rule record 501, classification 603 is set to theclassification 203 of the root thread execution data record 201 of thereceived transaction trace and the measure value 605 is set to themeasure value extracted by step 707. Following step 710 determines thetimeslot 604 of the measure record, e.g. using the start or end time ofthe transaction in case the measure represents the transaction trace asa whole, like the number of exceptions or the number of method calls, orthe start or end time of a specific method execution in case the measuredescribes a specific method execution. To ease storage, management andprocessing of measures, a monitoring and alerting system may use adiscrete time system, which segments continuous time into discretesegments or timeslots. Measures extracted from transactions describeevents like method executions performed on arbitrary points in time.This measure timestamps have to be assigned a corresponding timeslot incase of a discrete time system. After step 710, the process continueswith step 711.

Measure distribution records 801, which may be used to describe thestatistical distribution of measures of a specific type andclassification, are described in FIG. 8 a. A measure distribution record801 may contain but is not limited to a measure type 802 describing thetype of measures on which the distribution description is based, aclassification 803 describing the transaction trace class for which themeasure distribution is calculated, a distribution type describing thestatistical kind of distribution according to the statistical type ofmeasure value, which may be a distribution type based on discreteevents, like the occurrence of failed transaction, or a distributionbased on continuous measures, like execution times, a number of samplesfield 805 and a list of distribution parameters 806. The distributiontype 804 may be used to choose the appropriate statistical test tocompare different measure distribution records of the same measure type,classification and distribution type. Number of samples 805 mayinfluence the accuracy and confidence of such a test.

A distribution parameter record 810 as described in FIG. 8 b may be usedto describe a parameter of a concrete measure distribution and maycontain but is not limited to a type 811 identifying the type of theparameter, like mean of the distribution, min value, max value, range,expected failure rate or a specific quantile and a value 812 containingthe concrete distribution parameter value.

A statistical statement record 901, which is used to store datadescribing the result of a statistical test that compared two differentmeasure distribution records of the same measure type 802,classification 803 and distribution type 804, is shown in FIG. 9.

Such statistical tests compare a measure distribution record 801describing a distribution of a specific measure type and classificationfor a historic reference period or baseline with a measure distributionrecord 801 describing the current statistical behavior of a measure withthe same measure type and classification.

A statistical statement record 901 may contain but is not limited to ameasure type 902, and a classification 903 identifying type andclassification of the measures of the compared measure distributionrecords, a test type 904 identifying the performed statistical testwhich may e.g. indicate that a test to identify statisticallysignificant deviation of a specific quantile has been performed, atimeslot 905 identifying the point in time when the test was executed,and an anomaly indicator 906 indicating the detected deviation relativeto the baseline. The anomaly indicator may indicate a currentstatistical value significantly above or below the baseline,significantly near the baseline or an insignificant test result, wherean insignificant test result indicates statistical uncertainty if ananomaly occurred or not.

A conceptual description of the processes for statistic based anomalydetection is shown in FIG. 10. The cyclical update of historic baselinedata is shown in FIG. 10 a and the update of statistic data describingthe current distribution of the measure values and the statistical testto detect deviations between current and baseline distribution is shownin FIG. 10 b. Individual statistical baseline and current data iscalculated for each measure type and classification.

The baseline update process starts with step 1001 when a specified timeafter the last baseline update (e.g. 24 hours) has been elapsed.Following step 1002 fetches historic measures for the new baselineperiod from the measure repository 117. Examples of a baseline periodinclude all measures from yesterday or all measures from the same daylast week. Afterwards step 1003 calculates statistical distributionparameters representing the statistical measure value distribution ofthe new baseline period. Such distribution parameters may contain butare not limited to the mean value of the distribution, its minimum ormaximum value, standard deviation, expected value, or specific quantileslike the 0.5 or 0.9 quantile. The calculated distribution parameters maybe modified by applying a tolerance value. The applied tolerance valuechanges the baseline distribution parameters in a way to describe aslightly weaker performance behavior as calculated to make thestatistical analysis process based on the baseline parameters moreresistant against false positive alerts. The tolerance value may becalculated as a maximum of an absolute tolerance, a relative toleranceand a parameter specific tolerance.

The absolute tolerance is used to avoid statistically correct butirrelevant alerts for very fast transactions. As an example, if anend-to-end transaction with baseline response time of 10 ms shows acurrent response time of 20 ms or 25 ms, this represents a statisticallysignificant performance decline, but for real world users suchperformance degradations on a whole end-to-end transaction are barelynoticeable. By e.g. applying an absolute tolerance value of 50 ms andraising the baseline from 10 ms to 60 ms, such irrelevant alerts areavoided.

The relative tolerance is calculated by increasing a calculatedparameter value by a specific percentage.

The parameter specific tolerance may as an example, for a quantile basedparameter value use a less restrictive quantile than the calculatedquantile.

Exemplary values for absolute, relative and parameters specifictolerance for a quantile based parameter would be 50 ms, 20% and 0.05.For a calculated or estimated 0.9 quantile of 300 ms, the absolutetolerance level would be the calculated 300 ms plus the 50 ms of theabsolute tolerance resulting in 350 ms, the relative tolerance levelwould be the measured 300 ms plus 20% of the measured value resulting in360 ms. The parameter specific tolerance value for quantile parameterswould be applied by using the quantile with the index of the originalquantile 0.9 plus the parameter specific tolerance value 0.05 whichwould result in using the value of quantile 0.95 instead of the value ofquantile 0.9. The calculated or estimated value of quantile 0.95 maye.g. be 400 ms.

As the maximum of the three calculated tolerance levels is 400 ms, thevalue 400 ms is used as the new baseline parameter value.

After the baseline values were calculated and adapted by step 1003, thevalues 812 of the distribution parameter records 810 of thecorresponding measure distribution record 801 is updated with theadapted values in step 1004 and the process ends with step 1005. Thefield number of samples 805 is set to the number of measure records 601representing the new baseline period.

For new installed monitoring systems, the measure repository may duringthe first hours or days not contain sufficient historic measurement datato cover a full baseline period. In such situations, the used baselinewindow may be gradually increased until historical data for a fullbaseline time window is available.

The statistical processing of new measure records 601 by the anomalydetector 119 to update corresponding measure distribution records 801describing the current statistical distribution of measure values for aspecific measure type and classification, and to perform statisticaltests against corresponding statistical baseline parameters is shown inFIG. 10 b.

The process starts with step 1010, when the anomaly detector 119 startsprocessing a new measure record 601. Subsequent step 1011 fetches themeasure distribution record 801 with measure type 802 and classification803 matching the measure type 602 and classification 603 of the newmeasure record from the current distribution repository 120. Step 1012recalculates and updates values 812 of the distribution parameterrecords 810 of the measure distribution record 801 representing thecurrent distribution of the measure values, considering also the measurevalue 605 received with the new measure record 601. This recalculationmay contain but is not limited to recalculate mean, minimum and maximumvalues and to update quantile estimations, considering the new measure.The field number of samples 805 is incremented.

Following step 1013 fetches the corresponding measure distributionrecord 801 representing the baseline distribution from the baselinerepository 122 and step 1014 performs a statistical test appropriate forthe measure type of the new received measure record. The test analyzesboth baseline and current distribution parameters to determine withstatistical significance if the current distribution shows a weaker,better or equal performance behavior compared with the baselinedistribution. In case of estimated statistical parameters, like e.g.quantiles, the number of samples 805 and a desired confidence level maybe used to calculate a confidence interval of the estimated value. Thecorresponding “real value” of an estimated value is within theconfidence interval with a probability defined by the confidence level.Tests based on such estimated statistical parameters may also considerthis confidence interval. An absolute number of required samples may berequired for both exactly calculable parameters like mean and estimatedparameters. In case either number of samples 805 of baseline or currentdistribution is below this absolute threshold, the statistical testindicates an insignificant result.

The statistical test may produce a statistically significant resultindicating slower, faster or equal current behavior compared to thebaseline or an insignificant result, if e.g. the number of baseline orcurrent samples is too low.

Following step 1015 creates a statistical statement record 901 and setsmeasure type 902 and classification 903 according to the correspondingvalues of the received measure record 601. The test type 904 is setaccording to describe the statistical test performed in step 1014.Example test types would be “0.9 quantile test”, “0.5 quantile test” fortests detecting a deviation of specific quantiles of transactionresponse times or “Poisson test” for a test detecting deviations oftransaction failure rates.

Subsequent step 1016 sets the anomaly indicator 906 of the createdstatistical statement record 901 according to the result of the testperformed in step 1014, determines and sets the timeslot of the createstatistical statement record.

In case the test produced a significant result, the distributionparameter records 810 of the measure distribution record 801 describingthe current distribution are reset to clear data describing the currentdistribution. Future received measure records 601 are used toincrementally update the now reset measure distribution record 801 todescribe a distribution considering the time period since the lastsignificant test result. Also data used for parameter estimations arereset and number of samples is set to 0. If multiple statistical testsare performed for a specific measure type/classification combination,which may become significant on different points in time, separate,independent measure distribution records 801 describing the currentmeasure distribution must be maintained for each test which can thenalso be reset independently.

In case step 1017 identifies that the test produces no significantresult, step 1018 is omitted.

Subsequent step 1019 inserts the created statistical statement record901 into the statistical statement repository 121 for furtherprocessing. The process ends with step 1020.

Steps 1011 to 1012 of this process which update the measure distributionrecord describing the current distribution of a measure, have to beexecuted on every received measure record, whereas steps 1013 to 1020may be executed in a decoupled, cyclical way, that e.g. performs thestatistical testing only once per minute to save CPU resources on themonitoring node.

An alert transition rule 1101 which may be used by the alert processingand visualization module 123 to decide when to raise or lower an alert,based on incoming statistical statement records 901, is shown in FIG.11.

An alert transition rule 1101 may contain but is not limited to ameasure type 1102 identifying a specific kind of measure, a specifictest type 1103, a classification 1104 and a current alert state 1105 forwhich the alert transition rule is valid. Further, an alert transitionrule 1101 may contain a target alert state 1106 specifying the state ofan alert if the rule is applied, and a set of transition prerequisites1107 defining the conditions which have be fulfilled to apply the alertstate transition. The classification 1104 of an alert transition rule1101 may in some cases be set to a value indicating that it is valid forall classifications to provide broader but application andclassification independent alert transition rules.

An alert record 1201 representing an alert raised by the alertprocessing and visualization module 123 based on incoming statisticalstatement records and matching alert transition rules 1101 is describedin FIG. 12.

An alert record 1201 may contain but is not limited to a measure type1202, a test type 1203 and a classification 1204 to identify the measureon which the alert is based and the statistical test which was used todetect the condition that triggered the alert. Additionally, an alertrecord may contain an anomaly indicator 1205 identifying the detectedanomaly condition, an alert start time 1206 and an alert end time 1207describing the duration of the condition that caused the alert.

The processing of incoming statistical statement records 901 todetermine if alerts should be raised or lowered, as performed by theanomaly alerting and visualization module 123 is shown in FIG. 13.

The process starts with step 1300, when a new statistical statementrecord 901 is received. Subsequent step 1301 queries the alertrepository 125 for an open alert record 1201 with the same measure type1202, classification 1204 and test type 1203 as the received statisticalstatement record 901. An alert record is open, when the alert end time1207 of the record is not set. In case a matching open alert record isfound, the process continues with step 1303 which checks if anomalyindicators of the incoming statistical record and the existing alertrecord match. In case of a match no update to the alert record isrequired and the process ends with step 1312.

Otherwise, step 1304 fetches matching statistical statement records fromthe statistical statement repository 121 that are not related to anearlier alert to get the history of statistical statements for thismeasure and this test type which is not covered by an earlier alertrecord. This may be performed by selecting statistical statement recordswith matching measure type, classification and test type that areyounger than the youngest alert end time 1207 of an alert record withmatching measure type 1202, test type 1203 and classification 1204.

Subsequent step 1305 fetches from the alert rule repository 124 alerttransition rules 1101 with matching measure type 1102, test type 1103and classification 1104 and with a current alert state 1105 matching theanomaly indicator 1205 of the open alert record found in step 1301. Thetransition prerequisites of the fetched alert transition rules areevaluated to determine if an alert update is required. Such a transitionprerequisite may e.g. define that a raised alert indicating aperformance anomaly can only be lowered after a minimal number ofstatistical statements indicating normal performance behavior wasreceived.

In case alert transition rule evaluation indicates no required update ofthe alert record 1201, the process ends with step 1312. Otherwise, thealert record is updated in step 1307 and the change of the alert stateis notified. In case the alert state transition rule evaluation resultedin an end of the anomaly described by the alert record, also the alertend time 1207 is set. The process ends with step 1312.

In case step 1301 finds no matching open alert record 1201 in the alertrepository 125, the process continues with step 1308, which fetchesmatching statistical statement records 901 from the statisticalstatement repository 121 that are not covered by older, closed alertrecords. Subsequent step 1309 fetches alert transition rules 1101 withmatching measure type 1102, test type 1103 and classification 1104, anda current alert state 1105 indicating no existing open alert record.

The fetched alert transition rules are evaluated to determine if a newalert record should be created. In case no new alert record is required,the process ends with step 1312. Otherwise, step 1311 is executed whichcreates a new alert record 1201 with data form the incoming statisticalstatement record, and sets its alert start time 1206 to the currenttime. The alert creation is notified and the new alert is inserted intothe alert repository 125.

FIG. 14 compares variants of probability density functions of end-to-endtransaction response times, similar to measured and recorded densityfunctions of transaction response times of monitored real end-to-endtransactions.

Most perceived distributions show a statistical skewness to the right,where the majority of measures is faster than the mean value of thedistribution, and only a small amount of outliers is present withrelatively slow response times.

FIG. 14 a shows a normal performance situation 1401, with distributionparameters 0.5 quantile 1404 a, mean 1405 a and 0.9 quantile 1406 a.

FIG. 14 b shows a situation where the performance of the majority ofrequests remains unchanged, but the number of outliers with very slowresponse times is increased 1402. Distribution parameter 0.5 quantile1404 b is barely influenced by the outliers, but the mean value 1405 bshows sensibility against those outliers and changes significantly. As aconsequence, anomaly detection based on a mean value alone would in such(frequent) situations tend to create false positive alerts. The value of0.9 quantile 1406 b which monitors the slowest transactions is increasedas expected.

FIG. 14 c shows a situation where not the performance of a small set ofindividual transactions, like outliers, is decreased, but theperformance of the majority of transactions 1403. In this situation,both the 0.5 quantile 1404 c and the mean 1405 c show a deviation to thenormal situation 1401. As the set of outliers with extremely slowtransaction execution times is not changed, the 0.9 quantile 1406 cremains relatively stable.

A test based on a 0.5 quantile or a quantile near the level of 0.5 maybe used to determine the performance situation of the majority ofexecuted transactions compared to the baseline. A test based on the 0.9quantile or near this level, may be used to determine the performancesituation of outliers.

The process of estimating quantiles describing a baseline distributionis shown in FIG. 15. The nature of exact quantile calculation whichrequires a sorted list of all measurements, the large amount ofmeasures, and the requirement to calculate quantiles in near-real-time,does not allow an exact calculation. An accurate estimation method thatrequires a limited amount of memory and that only requires examiningeach measure once for the estimation is needed. Additionally,computational complexity and processing time to incorporate a newmeasure should be constant. I.e. adding a new measure to an estimatoralready considering 10.000 other measures should take the same time asadding it to an estimator only considering 100 other measures.Algorithms that fulfill these requirements are called one-pass,space-efficient quantile estimation algorithms.

The p-square algorithm (for a detailed description see e.g.http://www.cs.wustl.edu/˜jain/papers/ftp/psqr.pdf) is such an algorithmand may be used by described embodiments to estimate quantiles forbaseline and current distributions. Various other quantile estimationalgorithms that fulfill aforementioned requirements are available andknown in the art, like the algorithm proposed by John C. Liechty et.al., see e.g.http://www.personal.psu.edu/users/j/x/jxz203/lin/Lin_pub/2003_StatComp.pdf.Other quantile estimation algorithms may be used by embodiments of thecurrent disclosure without leaving scope and spirit of the application,as long as they fulfill the aforementioned requirements for an quantileestimator that operates on large sets of data and should providequantile estimations in real-time or near real-time.

The process starts cyclically with step 1501, if e.g. a specific timesince the last baseline update is elapsed. Subsequent step 1502 fetchesthe measure records 601 representing the new baseline period andfollowing step 1503 uses an implementation of a quantile estimator likea p-square algorithm to perform a fast and memory efficient calculationof the quantile estimations describing the baseline distribution. Thequantile estimator may store selected data describing the distributionin form of supporting points describing an estimated curve progressionof the quantile function. The data stored by the quantile estimator alsoallows the addition of different quantile estimations representingdifferent time frames to one quantile estimation representing thecombined time frame. This may be used to optimize baseline quantilecalculation if a set of quantile estimations describing smaller timeframes covering the whole baseline timeframe is already available. Itallows e.g. to add 24 quantile estimations describing different hours ofa day into one quantile estimation describing the whole day.

After calculation of the quantile estimation is finished, the processcontinues with step 1504 which stores the quantile estimations incorresponding measure distribution records 801. The process ends withstep 1505.

The baseline update process is performed for each measure type andclassification.

The update of quantile estimations describing the current measuredistribution of measures with a specific measure type and classificationis shown in FIG. 16.

The process may be executed as part of step 1012 of the processdescribed in FIG. 10 b, it is started with step 1601 when a new measurerecord is received and the matching measure distribution record 801 isfetched from the current distribution repository 120. Subsequent step1602 adds the new measure value to the quantile estimator, which usesthe new measure to adapt its internal representation of the quantilefunction to reflect the new received measure. Following step 1603 storesthe update quantile estimation data in the measure distribution record801. The process then ends with step 1604.

The process of comparing a baseline and a current measure valuedistribution based on estimated quantile values is shown in FIG. 17.

The process starts with step 1701 when an updated measure distributionrecord 801 describing a current measure distribution, containing updatedquantile estimations is available (e.g. after execution of the processdescribed in FIG. 16). Subsequent step 1702 fetches the matching measuredistribution record 801 describing the corresponding baselinedistribution from the baseline repository 122 and then fetches the valuefor the quantile used by the test from the measure distribution record801 describing the baseline distribution.

Following step 1703 applies absolute, relative and parameter specifictolerances to the baseline quantile value as described in step 1003 ofthe more generic process 10 a. In the generic description, tolerancesare already calculated and applied on baseline updates and a baselinewith added tolerances is stored. In the quantile specific processing,plain baseline data is created and stored without added tolerances. Thisallows using the same baseline quantile estimation data for differentquantile tests using different tolerance values. Both variants arepossible without leaving the spirit and scope of the current disclosure.

Following step 1704 fetches the quantile value (e.g. 0.5 quantile or 0.9quantile) required for the test from the measure distribution record 801describing the current measure distribution.

Step 1705 evaluates if number of samples 805 of current and baselinedistribution record 801, and the distance between expected quantile(=baseline quantile with applied tolerance) and the current quantileallow a significant test result. The smaller the difference betweenexpected and observed quantile, the more samples are required todetermine a statistically significant difference between them.

In case number of samples and compared quantile values allow testing,step 1705 continues with calculating a confidence interval for theestimated current quantile, defining a range in which the real currentquantile lies with a specific, defined probability (e.g. 99%). The sizeof the confidence interval depends on the number of samples 805 ofcurrent and baseline distribution, and the desired confidence(probability that the real value lies within the interval). In case theexpected baseline quantile value lies outside of the confidence intervalof the current quantile value, a significant deviation was detected andthe process continues with step 1707.

Step 1707 creates a statistical statement record indicating a deviationbetween baseline and current distribution. In case the higher bound ofthe confidence interval of the current quantile is lower than thebaseline quantile with applied tolerance, the current quantile value isbelow the baseline with applied tolerance with required statisticalcertainty, and a statistical statement 901 indicating a normalperformance behavior is created. In case the lower bound of theconfidence interval of the current quantile is higher than the baselinequantile value with applied tolerance, the current quantile is higherthan the baseline with required statistical certainty, and a statisticalstatement record indicating abnormal, degraded performance behavior iscreated.

Subsequent step 1712 clears the quantile estimator of the currentdistribution to start new quantile calculations not considering oldmeasurements. The process then ends with step 1713.

In case step 1705 either detects that there is no significant deviationbetween current and baseline quantile, e.g. the baseline quantile withapplied tolerance lies within the confidence interval of the currentquantile, or detects that the number of samples is not sufficient for asignificant test, the process continues with step 1708, which evaluatesthe number of considered samples again to determine if a test resultindicating a current quantile value being equal to the baseline quantilewith applied tolerance is legitimate.

In case a result indicating an equal current and tolerance adaptedbaseline quantile is justified, a statistical statement record 901 withan anomaly indicator indicating a current distribution on the edge ofthe baseline is created by step 1710. The process then continues withstep 1712 to clear the quantile estimation data for the currentdistribution.

In case of insufficient samples, the process continues with step 1711 bycreating a statistical statement record 901 indicating an insignificanttest result. In this case, the quantile estimation data for the currentdistribution is not reset and the process ends with step 1713.

A situation where performance anomaly detection based on a singlequantile estimator for a specific quantile may detect a performanceanomaly with undesired delay, and an approach to overcome this problemby using two independent estimators, is shown in FIG. 18.

Both FIG. 18 a and FIG. 18 b shown the same performance situation, wherethe measured performance gradually approaches the baseline and thenstays near the baseline for a considerable time. Afterwards, abruptperformance degradation is observed.

FIG. 18 a shows the detection of this degradation with a testing processbased on a single quantile estimator, which is delayed due to a quantileestimator for the current distribution considering a long measurehistory and thus is unable to react fast on sudden changes of theperformance situation.

A baseline level 1802, describing the tolerance modified value of aspecific estimated quantile from the baseline distribution is testedagainst a corresponding estimated quantile value 1803 describing thedistribution of the current measures 1801.

The current measures 1801 and the corresponding estimated quantilevalues gradually become slower, until a statistical test according tothe process described in FIG. 17 detects that baseline quantile andcurrent quantile are equal 1804 a. Afterwards, measure values andcurrent baseline stay very near to the baseline level for a considerabletime. As a consequence, the test is unable to produce anothersignificant result to reset the estimation data, because the number ofsamples is too low for the relative small difference between current andbaseline quantile estimation value. The estimator aggregates a largeamount of samples confirming the last significant test result of acurrent quantile equal to the baseline, which causes the estimator toreact slow on abrupt performance changes of the original measured valuesas shown in 1809.

The quantile describing the current distribution only rises slowly afterthe abrupt change, causing a delayed detection of the performanceanomaly.

FIG. 18 b shows the same situation with an enhanced detection mechanismusing two different quantile estimators being reset on different events.The enhanced mechanism detects such abrupt performance changes faster.

Same as with the single quantile estimator testing process, the firstquantile test gets significant 1804 a as the quantile estimated by thefirst estimator 1803 and the measures 1801 approach the baseline 1802.However, on significance, also the second quantile estimator gets reset.Tests based on the second estimator are performed cyclically, e.g. everyminute, 1805 a to 1805 e, and the second estimator is reset if the testgenerates the same result as the last significant test using the firstestimator 1804 a. During unchanged performance behavior of the measurevalues, the quantile estimations of the second estimator 1807 a to 1807e are relatively similar to the estimations of the first estimator.However, after abrupt performance degradation 1809, the second quantileestimator is fast reacting and quickly provides a quantile valuesignificantly over the baseline 1808, which is detected and notified1806.

FIG. 19 describes processes that may be used to implement a statisticaltesting system based on two different quantile estimators as drafted inFIG. 18.

A process that additionally updates quantile estimation data for thesecond, fast quantile estimator on incoming measure records is shown inFIG. 19 a.

The process starts with step 1901, when a new measure record isreceived, and a measure distribution record used to store the fastquantiles is fetched. Similar to the process described in FIG. 16, thenew measure value is added to the estimation base of the quantileestimator (see step 1902) and afterwards the update quantile estimationdata is stored in the measure distribution record described by the fastquantile estimator 1903. The process then ends with step 1904.

A process that cyclically performs a statistical test both consideringslow and fast quantile estimator is shown in FIG. 19 b.

The process is started with step 1910 when a defined time (e.g. 1minute) since the last test execution is elapsed. Subsequent step 1911performs the test described in steps 1705 and 1708 of FIG. 17 to comparea tolerance adjusted baseline quantile with a quantile describing acurrent distribution for the quantiles of both estimators. In case thetest for the slow quantile is significant, the process continues withstep 1913 which creates and stores a statistical statement recordaccording to the test result using the slow estimator. Subsequent step1914 resets quantile estimation data for slow and fast quantileestimator and the process ends with step 1918.

In case the test performed in step 1911 provides no significant resultfor the slow quantile estimator, the process continues with step 1915,which checks if the test using the fast quantile estimator issignificant and the test result is different to the last significanttest result using the slow quantile estimator. In this case, the processcontinues with step 1913 which creates and stores a statisticalstatement record reflecting the test result using the fast quantileestimator.

In case the test using the fast quantile estimator is either notsignificant or the test result is equal to the last significant testresult using the slow quantile estimator, the process continues withstep 1916, which checks if the test result of the fast quantileestimator equals the last significant test result using the slowquantile estimator, regardless if the test using the fast estimator issignificant or not. In case both results are equal, the estimation baseof the fast quantile estimator is cleared in step 1917. The process endswith step 1918.

A failure detection rule record 2001 which may be used to determine if areceived end-to-end transaction trace describes a failed transactionexecution is shown in FIG. 20. A failure detection rule record maycontain but is not limited to a transaction filter rule 2002, which maybe used to determine if a specific transaction trace can be analyzed bythe rule and a failure condition extraction rule 2003, which determinesthe conditions that identify a failed transaction. An exemplary failuredetection rule dedicated to analyze end-to-end transaction tracesinitiated by HTTP requests may have a transaction filter rule fortransactions with a first method execution dedicated to handle anincoming HTTP request. The failure condition extraction rule may examinethe response code returned by the method that handled the incoming HTTPrequest and provided the corresponding response. In case the return codeindicates a server internal error, the end-to-end transaction may beflagged as failed. Various other failure detection rules 2001 may bedefined, e.g. based on uncaught exceptions detect in a monitoredtransaction, or the detection of a specific method call in a transactiontrace know to create an error response.

The process of determining if an incoming end-to-end transaction isfailed, as performed e.g. by the transaction classifier 113, is shown inFIG. 21.

The process starts with step 2101, when a new transaction trace isreceived. Subsequent step 2102 fetches the first failure detection ruleand following step 2103 evaluates the transaction filter rule 2002. Incase the transaction does not match, the process continues with step2105 to fetch the next failure detection rule. In case no other rule isavailable, the failure indicator 206 of the transaction trace is set toindicate a successful transaction in step 2109 and the process ends withstep 2111. In case another failure detection rule is available, theprocess continues with step 2103.

In case the transaction matches the filter rule 2002, the processcontinues with step 2104, which evaluates the failure conditionextraction rule 2003. In case no failure is detected, the processcontinues with step 2105. Otherwise, step 2110 is executed, which setsthe failure indicator 206 to indicate a failed transaction and theprocess ends with step 2111.

The process of measuring the number of monitored transactions and thenumber of failed monitored transactions as e.g. performed by the measureextractor 116 is shown in FIG. 22.

The process starts with step 2201, when a new classified and failurechecked transaction trace arrives at the measure extractor. Subsequentstep 2202 creates a measure record 601, sets its measure type 602 toindicate a transaction count measure, its classification 603 to theclassification of the transaction trace and sets the measure value 605to 1, indicating one additional transaction.

Following step 2203 checks if the failure indicator 206 indicates afailed transaction. In case of a failed transaction, step 2204 isexecuted, which creates a measure record 601, sets the measure type 602to indicate a failed transaction count measure, classification 603 tothe classification of the transaction trace and sets the measure value605 to 1, indicating one additional failed transaction.

Step 2205 is executed whether the transaction is failed or not. Itdetermines and sets the time slot 604 of the previous created measurerecords 601 and inserts them to the measure repository 117. The processends with step 2206.

An alternative processing could create one measure record perclassification and times slot. In case a new transaction is processed,the measure record for the current classification record and time slotis fetched, and its measure value is incremented by one.

The process of updating the measure distribution records 801representing the failure rate baselines for all classifications is shownin FIG. 23.

The process is started with step 2301, when a specific time since thelast baseline update is elapsed. Typical baseline update intervals areone hour, 12 hours or one day. Following step 2302 fetches measurerecords 601 with measure type transaction count and failed transactioncount for the current classification and with a time slot in the newbaseline period. Typical base line periods are one hour, 12 hours or oneday.

Following step 2303 determines the number of transactions and failedtransactions in the new baseline period by evaluating the fetchedmeasure records and calculates the raw failure rate of the baseline bydividing the failed transaction count by the transaction count.

Step 2304 applies an absolute and a relative tolerance value to the rawfailure rate and subsequent step 2305 analyzes transaction count andfailure rate to determine the appropriate statistical distribution modelfor the baseline. In case of a high number of transactions and a lowfailure rate, a Poisson distribution may be chosen. Otherwise a Binomialdistribution is used.

The selected distribution model is parameterized in step 2306 with themeasured transaction count and failed transaction count to describe adistribution showing the same failed transaction count as the baselineat the same number of transactions as the baseline. Following step 2307uses the parameterized distribution model and a confidence levelspecified for the failure rate to calculate a confidence interval forthe baseline failure rate.

Step 2308 fetches the measure distribution record 801 for the currentclassification and for the failure rate distribution from the baselinerepository 122 and subsequent step 2309 stores the upper bound of theconfidence interval calculated in step 2307 as new baseline failure ratein the measure distribution record. The process then ends with step2310.

The process which determines the current failure rate distribution, andtests if the current failure rate deviates significantly from thebaseline failure rate is shown in FIG. 24.

The process is performed cyclically with an execution frequency thatprovides fast notifications, but which also provides a high probabilitythat monitored transaction execution have been performed and finishedbetween two test executions. An execution frequency of one execution perminute represents a good compromise. The process is performed for allclassifications.

The process starts with step 2401, when the time between two tests iselapsed. Subsequent step 2402 fetches the measure distribution record801 for failure rate testing for the current classification describingthe current failure rate distribution from the current distributionrepository 120. The measure distribution record may contain the value ofthe transactions and failed transactions performed since the lastsignificant test result and up to the last test execution. Followingstep 2403 fetches all measure records 602 for measure type 602“transaction count” and “failed transaction count” with a time slot 605within the time since the last performed failure rate test, e.g. lastexecution of this process, and for the current classification from themeasure repository 117. Step 2404 uses the fetched measure records todetermine the number of transactions and failed transactions receivedsince the last test execution, and then performs a corresponding updateof the measure distribution record fetched in step 2401 by adding thenumber of transactions and failed transactions since the last test tothe number of transactions and failed transactions stored in the measuredistribution record. Following step 2405 checks if the number oftransactions now stored in the measured distribution record is above anabsolute minimum of transactions required for a statistical test. Thethreshold for sufficient transactions may e.g. be 30 transactions. Incase not sufficient transactions have been recorded since the lastsignificant test result, the process continues with step 2406, whichcreates a statistical statement record indicating an insignificant testand the process ends with step 2407.

If otherwise sufficient transaction executions are available, theprocess continues with step 2408, which fetches the measure distributionrecord representing the baseline distribution for failure detection forthe current classification from the baseline repository 122. Subsequentstep 2409 fetches a matching distribution model for the current failurerate distribution, based on the current number of transactions and theexpected failure rate. The distribution model may be selected from aBinominal or a Poisson distribution model. Both distribution models arestatistically modelling processes with discrete binary outcome (e.g.transaction failed or passed, see also Bernoulli trial). The onlydifference between the two models is that the Binominal model providesan exact model and the Poisson model, which is mathematically lesscomplex, provides an approximation assuming an infinite number ofsamples. If the number of transactions (samples) exceeds a specificthreshold, the process switches from a Binominal to a Poisson model. Theexpected failure rate is the failure rate stored in the measuredistribution record 801 describing the baseline failure rate. Followingstep 2410 parameterizes the selected distribution model with the currenttransaction count and the baseline failure rate. Parameterizing theselected distribution model with current transaction count and baselinefailure rate translates the measured baseline failure rate into a modelof a statistical distribution that describes the failure rate of thebaseline distribution under the conditions (number of samples) of thecurrently perceived transaction samples. This parameterized statisticalmodel may be used to compare the observed current failure rate with anexpected failure rate derived from the baseline observations.

Subsequent step 2411 calculates a lean confidence interval for theexpected failure rate (smaller range), using a specified lean confidencelevel and a strict confidence interval (larger range) using a specifiedstrict confidence level. Upper and lower bounds of the confidenceintervals are used to test against the current failure rate in a waythat avoids too early failure rate alerts, and that allows quicklowering of failure rate alerts in case the failure rate falls back tothe baseline level. Subsequent step 2412 calculates the current failurerate by dividing the current number of failed transactions by the numberof current transactions.

Step 2413 checks if the calculated current failure rate is higher thanthe upper bound of the strict confidence interval. In case it is higher,the process continues with step 2414 which creates and stores astatistical statement record 901 indicating a failure rate above thebaseline for the current classification and subsequent step 2415 resetsthe current transaction count and current failed transaction count ofthe measure distribution record 801 describing the current failure ratedistribution. The process then ends with step 2416.

In case the calculated current failure rate is not higher than thestrict upper bound, the process continues with step 2417, which checksif the current failure rate is between the upper strict and the upperlean confidence level. In this case, the test cannot significantlydetermine if the failure rate is above or at the baseline and creates astatistical statement record 901 indicating an insignificant test withstep 2418, the process ends with step 2419.

In case the current failure rate is not between strict and lean upperconfidence level, the process continues with step 2420 which checks ifthe current failure rate is between the upper and lower lean confidencelevel. In this case, a statistical statement record 901, indicating acurrent failure rate at the baseline failure rate level is created instep 2422 and following step 2423 resets the current failure ratedistribution description. The process then ends with step 2424. In casethe current failure rate is below the lower lean confidence level, theprocess continues with step 2421 which creates a statistical statementrecord 901 indicating a current failure rate below the baseline andfollowing step 2423 resets the measure distribution record 801describing the current failure rate distribution. The process then endswith step 2424.

The techniques described herein may be implemented by one or morecomputer programs executed by one or more processors. The computerprograms include processor-executable instructions that are stored on anon-transitory tangible computer readable medium. The computer programsmay also include stored data. Non-limiting examples of thenon-transitory tangible computer readable medium are nonvolatile memory,magnetic storage, and optical storage.

Some portions of the above description present the techniques describedherein in terms of algorithms and symbolic representations of operationson information. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. These operations, while described functionally or logically, areunderstood to be implemented by computer programs. Furthermore, it hasalso proven convenient at times to refer to these arrangements ofoperations as modules or by functional names, without loss ofgenerality. It is understood that grouping of operations within in agiven module is not limiting and operations may be shared amongstmultiple modules or combined into a single module.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the described techniques include process steps andinstructions described herein in the form of an algorithm. It should benoted that the described process steps and instructions could beembodied in software, firmware or hardware, and when embodied insoftware, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a tangible computer readable storagemedium, such as, but is not limited to, any type of disk includingfloppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs), EPROMs, EEPROMs,magnetic or optical cards, application specific integrated circuits(ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatuses to perform the required method steps. Therequired structure for a variety of these systems will be apparent tothose of skill in the art, along with equivalent variations. Inaddition, the present disclosure is not described with reference to anyparticular programming language. It is appreciated that a variety ofprogramming languages may be used to implement the teachings of thepresent disclosure as described herein.

The present disclosure is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method for detectinganomalies in a performance metric associated with monitored transactionsof a distributed computing environment, comprising: capturing, by asensor instrumented in a monitored application, a plurality ofmeasurement values for a performance metric, where the performancemetric is for a transaction executed in part by the monitoredapplication, and the sensor and the monitored application reside on andare executed by a processor of a host computer; calculating, by theanomaly detector, a current distribution parameter from the plurality ofmeasurement values, where the current distribution parameter isindicative of statistical distribution of the plurality of measurementvalues for the performance metric during the present period of time;retrieving, by the anomaly detector, a baseline distribution parameterfor the performance metric, where the baseline distribution parameter isindicative of statistical distribution of measurement value for theperformance metric during a preceding period of time, where thepreceding period of time precedes the present period of time; anddetecting, by the anomaly detector, an anomaly in the performance metricby comparing the current distribution parameter to the baselinedistribution parameter, where the anomaly detector is executed by aprocessor of a monitoring computer residing across a network from thehost computer.
 2. The method of claim 1 further comprises generating analert by the computing device, the alert being generated in response todetecting an anomaly in the performance metric.
 3. The method of claim 1further comprises performing the steps of calculating, retrieving anddetecting in part while the transaction is being executed.
 4. The methodof claim 1 further comprises identifying an anomaly in the performancemetric when the current distribution parameter falls outside a toleranceof the baseline distribution parameter.
 5. The method of claim 1 furthercomprises determining whether quantity of the plurality of measurementvalues exceeds a significance threshold; and detecting an anomaly in theperformance metric only when the quantity of measurement values exceedsthe significance threshold.
 6. The method of claim 1 further comprisesinitializing value of the current distribution parameter when anomalydetection was performed unless the anomaly detection generated a resultindicating statistical uncertainty as to the existence or not of ananomaly.
 7. The method of claim 1 further comprises accumulating, by theanomaly detector, a preceding set of measurement values for theperformance metric, where the preceding set of measurement values wereacquired during execution of transactions in the preceding period oftime; calculating, by the anomaly detector, the baseline distributionparameter from the preceding set of measurement values; and storing, bythe anomaly detector, the baseline distribution parameter in arepository.
 8. The method of claim 1 wherein the baseline distributionparameter and the current distribution parameter are selected from thegroup comprising mean, median, minimum value, maximum value, standarddeviation, and quantile.
 9. The method of claim 1 wherein capturing aplurality of measurement values for a performance metric furthercomprises instrumenting bytecode for the monitored application with thesensor.
 10. The method of claim 1 wherein capturing a plurality ofmeasurement values for a performance metric further comprisesidentifying elements in a document object model that contains requestdirectives and instruments an identified element with the sensor, wherethe monitored application is further defined as a web browserinstrumented with a browser agent and the browser agent instruments theidentified element with the sensor.
 11. The method of claim 10 whereinthe request directive is defined as content update request directive orcontent load request directive.
 12. The method of claim 10 wherein therequest directive is defined as resource request directive.
 13. Themethod of claim 1 wherein calculating a current distribution parameterfurther comprises calculating a quantile for the plurality ofmeasurement values using an estimation method that examines eachmeasurement value in the plurality of measurement values only once. 14.The method of claim 13 wherein the estimation method is further definedas a p² algorithm.
 15. The method of claim 1 further comprisescalculating, by the anomaly detector, a second current distributionparameter from the plurality of measurement values, where the secondcurrent distribution parameter differs from the current distributionparameter and is indicative of statistical distribution of the pluralityof measurement values for the performance metric during the presentperiod of time; retrieving, by the anomaly detector, a second baselinedistribution parameter for the performance metric, where the secondbaseline distribution parameter is of same type as the second currentdistribution parameter and is indicative of statistical distribution ofmeasurement value for the performance metric during the preceding periodof time; and detecting, by the anomaly detector, another type of anomalyin the performance metric by comparing the second current distributionparameter to the second baseline distribution parameter.
 16. The methodof claim 1 wherein the current distribution parameter has a value thatchanges in response to an anomaly that affects a majority of themeasurement values for the performance metric.
 17. The method of claim16 wherein the current distribution parameter is defined as a 0.5quantile.
 18. The method of claim 1 wherein the current distributionparameter has a value that changes in response to an anomaly that causesoutliers of the performance metric.
 19. The method of claim 18 whereinthe current distribution parameter is defined as a 0.9 quantile.
 20. Themethod of claim 1 further comprises receiving, by a measure extractor, aplurality of transaction events resulting from transactions executed inthe distributed computing environment, where the transaction eventinclude a measurement value for at least one performance metric;grouping, by the measure extractor, transaction events in the pluralityof transaction events into a group of transaction events, where thetransaction events in the group of transaction event are fromtransaction that perform similar tasks; and extracting, by the measureextractor, measurement values for the performance metric from thetransaction events in the group of transaction events to form the groupof measurement values, where the measure extractor is executed by aprocessor of a computing device.
 21. The method of claim 20 furthercomprises grouping transaction events in the group of transaction eventsusing data extracted from a request that initiated the transactionassociated with the transaction events.
 22. The method of claim 21wherein grouping transaction events further comprises extracting auniform resource locator from the request, parsing the uniform resourcelocator to determine an identifier for a server to which the request wassent and a path identifying an addressed resource on the server, andgrouping transaction events using the identifier for the server to whichthe request was sent and the path identifying an addressed resource onthe server.
 23. A computer-implemented method for detecting anomalies ina performance metric associated with monitored transactions of adistributed computing environment, comprising: receiving, by atransaction monitor, a plurality of transaction events resulting fromtransactions executed in the distributed computing environment, wherethe transaction events include a measurement value for at least oneperformance metric and the measurement value was acquired duringexecution of transactions in a present period of time; grouping, by thetransaction monitor, transaction events in the plurality of transactionevents into a group of transaction events, where the transaction eventsin the group of transaction event are from transaction that performsimilar tasks; and extracting, by the transaction monitor, measurementvalues for a given performance metric from the transaction events in thegroup of transaction events to form the group of measurement valuescalculating, by the transaction monitor, a current distributionparameter from the measurement values in the group of measurementvalues, where the current distribution parameter is indicative ofstatistical distribution of the measurement values for the givenperformance metric during the present period of time; retrieving, by thetransaction monitor, a baseline distribution parameter for theperformance metric, where the baseline distribution parameter isindicative of statistical distribution of measurement value for theperformance metric during a preceding period of time, where thepreceding period of time precedes the present period of time; anddetecting, by the transaction monitor, an anomaly in the performancemetric by comparing the current distribution parameter to the baselinedistribution parameter, where the transaction monitor is executed by aprocessor of a computing device.
 24. A computer-implemented method fordetecting anomalies in transactions executing in a distributed computingenvironment, comprising: receiving, by a transaction monitor, atransaction event resulting from a transaction executing in thedistributed computing environment; classifying, by the transactionmonitor, the transaction into a given classification using dataextracted from a request that initiated the transaction; evaluating, bythe transaction monitor, a failure condition for the transaction usingdata contained in the transaction event; for the given classification,tracking, by the transaction monitor, a number of transactions executedin a present period of time and a number of failed transaction occurringin the present period of time; periodically computing, by transactionmonitor, a current statistical distribution model for failure rate oftransactions in the given classification, where the current statisticaldistribution model for failure rate is computed using the number oftransactions executed in the present period of time for the givenclassification and the number of failed transactions occurring in thepresent period of time for the given classification; retrieving, by thetransaction monitor, a baseline statistical distribution model forfailure rate of transactions in the given classification during apreceding period of time, where the preceding period of time precedesthe present period of time; and detecting, by the transaction monitor,an anomaly by comparing the current statistical distribution model tothe baseline statistical distribution model, where the transactionmonitor is executed by a processor of a computing device.
 25. The methodof claim 24 wherein evaluating a failure condition for the transactioncomprises examining a response code returned by a method that handledHTTP request.
 26. The method of claim 24 wherein evaluating a failurecondition for the transaction comprises examining uncaught exceptionsdetected during execution of the transaction.
 27. The method of claim 24wherein the current statistical distribution model is further defined asone of a Binominal distribution model or a Poisson distribution model.28. The method of claim 24 further comprises periodically updating thebaseline statistical distribution model using a number of transactionsexecuted in the preceding period of time and a number of failedtransactions occurring in the preceding period of time.
 29. An anomalydetection system that monitors transaction in a distributed computingenvironment, comprising: a sensor instrumented in a monitoredapplication residing on a host computer and executed by a processor ofthe host computer, the sensor generates one or more transaction eventsand sends the transaction events across a network to a monitoringcomputer; a transaction classifier residing on the monitoring computer,the transaction classifier is configured to receive a plurality oftransaction events resulting from transactions executed in thedistributed computing environment and, in response to receiving theplurality of transaction events, group a portion of the plurality oftransaction events into a group of transaction events, where thetransaction events in the group of transaction event are fromtransactions that perform similar tasks; a measure extractor residing onthe monitoring computer, the measure extractor is configured to receivethe group of transaction events and extract measurement values for agiven performance metric from the transaction events in the group oftransaction events to form the group of measurement values, where atransaction event includes a measurement value for the given performancemetric and the measurement value was acquired during execution of atransaction in a present period of time; and an anomaly detectorresiding on the monitoring computer and configured to receive the groupof measurement values, the anomaly detector calculates a currentdistribution parameter from the group of measurement values and detectsan anomaly in the given performance metric by comparing the currentdistribution parameter to a baseline distribution parameter, where thecurrent distribution parameter is indicative of statistical distributionof the measurement values for the given performance metric during thepresent period of time and the baseline distribution parameter isindicative of statistical distribution of measurement values for thegiven performance metric during a preceding period of time, where thepreceding period of time precedes the present period of time and thetransaction classifier, the measure extractor and the anomaly detectorare executed by a processor of the monitoring computer.
 30. The systemof claim 29 wherein the anomaly detector, in response to detecting ananomaly in the given performance metric, generates an alert.
 31. Thesystem of claim 29 wherein the anomaly detector identifies an anomaly inthe given performance metric when the current distribution parameterfalls outside a tolerance of the baseline distribution parameter. 32.The system of claim 29 wherein the anomaly detector initializes value ofthe current distribution parameter when anomaly detection was performedunless the anomaly detection generated a result indicating statisticaluncertainty as to the existence or not of an anomaly.
 33. The system ofclaim 29 wherein the anomaly detector accumulates a preceding set ofmeasurement values for the given performance metric, calculates thebaseline distribution parameter from the preceding set of measurementvalues, and stores the baseline distribution parameter in a repository,where the preceding set of measurement values were acquired duringexecution of transactions in the preceding period of time.
 34. Thesystem of claim 29 wherein the anomaly detector calculates the currentdistribution parameter by calculating a quantile for the group ofmeasurement values using an estimation method that examines eachmeasurement value in the group of measurement values only once.
 35. Thesystem of claim 34 where the estimation method is further defined as ap² algorithm.
 36. The system of claim 29 wherein the sensor isinstrumented in bytecode of the monitored application.
 37. The system ofclaim 29 where the monitored application is further defined as a webbrowser instrumented with a browser agent such that the browser agentidentifies elements in a document object model that contain a resourceload directive and instruments an identified element with the sensor.